Fine-tuned distilbert-base-uncased for 3-class sentiment classification (negative / neutral / positive) on the Financial PhraseBank dataset — short financial news sentences labeled by finance-background annotators.

Classifies the sentiment of short financial/business news sentences, e.g. company earnings reports, market commentary, or corporate announcements. Not intended for general-purpose sentiment analysis outside a finance context.

The model performs well on sentences with explicit sentiment language, but struggles on sentences requiring implicit domain reasoning — e.g. "the company will deliver X units to a buyer" implies a sale (positive) without using any positive-sounding words. It also has difficulty with sentences containing mixed or competing signals (e.g. two market indices moving in opposite directions within one sentence). The most common error pattern is misclassifying positive or negative sentences as neutral, likely influenced by class imbalance in the training data (neutral is the majority class).

This is a learning/portfolio project — trained on a public academic dataset, not intended for production financial decision-making.